Open AccessModelling and control for eliminating flux oscillations in generation IV high temperature gas cooled reactor
Author(s) -
Badgujar K.D.,
Park P.G.
Publication year - 2016
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2015.1749
Subject(s) - control theory (sociology) , neutron flux , continuous stirred tank reactor , flux (metallurgy) , temperature control , power (physics) , nuclear reactor core , nonlinear system , core (optical fiber) , oscillation (cell signaling) , xenon , control rod , process (computing) , mechanics , nuclear engineering , physics , materials science , engineering , computer science , neutron , control (management) , thermodynamics , chemistry , nuclear physics , biochemistry , optics , quantum mechanics , artificial intelligence , chemical engineering , metallurgy , operating system
The core power distribution in the advanced high temperature prismatic reactor (AHTPR 625) belonging to the high temperature gas cooled reactor family is controlled using the constrained receding‐horizon control algorithm. The outputs from each region of the reactor core are used as the inputs of the system. The reactor model is based on the two‐point‐coupled xenon oscillation model. It utilises the nonlinear xenon and iodine balance equations along with the one‐group, one‐dimensional neutron diffusion equation including feedback with a power coefficient. Axially, the reactor core is divided into two regions. Each region has one input and one output. These regions are coupled with each other. The proposed control method demonstrates very fast response to input flux change in the form of a step and ramp change without any residual flux oscillations between the regions of the reactor core. The constrained receding‐horizon control problem involves solving an optimisation problem for finite future samples and obtaining the control with few samples. Among the few constrained control samples only the first one is applied and the process to solve the optimisation problem is repeated again.